Images recorded in ground areas potentially containing surface laid land mines are considered. The first hypothesis is that the image is of clutter (grass) only, while the alternative is that the image contains a partially occluded (covered) land mine in addition to the clutter. In such a scenario, the occlusion pattern is unknown and has to be treated as a nuisance parameter. In a previous paper it was shown that deterministic treatment of the unknown occlusion pattern, in companion with the applied model, renders a substantial increase in detector performance as compared to employment of the traditional additive model. However, a deterministic assumption ignores possible correlation and additional gains could be possible by taking the spatial properties into account. In order to incorporate knowledge regarding the occlusion, the spatial distribution is characterized in terms of an underlying Markov Random Field (MRF) model. A major concern with MRF models is their complexity. Therefore, in addition to this, a less computationally demanding technique to accommodate the occlusion behavior is also proposed. The main purpose of this paper is to investigate if significant gains are possible by acknowledging the spatial dependence. Evaluation on data using real occluded targets however indicates that the gain seem to be marginal.
KEYWORDS: Land mines, Mining, Data modeling, Sensors, Finite element methods, Optical testing, Infrared cameras, Shortwaves, Temperature metrology, Aluminum
This paper presents preliminary analysis of the data from measurements on a minefield in Croatia done in the international cooperation project Airborne Minefield Area Reduction (ARC). Temperature differences above and around suspected mines and minefield indicators, were recorded with a long wave IR camera in 8-9 micrometers , over a time of several days, capturing data under different weather conditions. The data are compared to simulations of land mines, minefield indicators and other objects using a themodynamic FEM model, developed at FOI. Different detection methods are presented and applied to the data.
This paper presents activities concerning optical detection of landmines at FOI, former FOA. The work is focused on the understanding of the origin of detectable optical signatures for choosing the most favorable conditions for detection. Measurements in test beds and calculations using a thermodynamic FEM model with conditions similar to those of the measurements are compared and interpreted in order to explain the behavior of the contrast. Examples will be given on modeling of buried landmines in soil. The heat flow as well as moisture flow has been taken into consideration. The diurnal heat exchange between the soil surface and the atmosphere generates the contrasts in the infrared images. Calculated temperature differences between the background and the surface above the buried object are compared to measured data from experiments. Results are presented and show how the temperature differences can vary over a 24-hour period. The variation depends on the weather at the time as well as the weather before the measurements started. Results from processing and analysis of temporal variations of optical signals from buried landmines and backgrounds are presented as well as their relation to weather parameters. A detection approach including the Likelihood Ratio Test (LRT) is presented. Some of the work has been carried out in an international cooperation project, Airborne Minefield Area Reduction (ARC). The objective is to develop, demonstrate and promote a new system for performing the UN Level 2 surveys allowing a quick reduction of suspected mine polluted areas and post cleaning quality control.
A parametric model for the infrared signature caused by a buried land mine is presented. Further, two ways of modeling the colored background noise, is proposed. In the first, it is assumed the noise can be approximated by an autoregressive process, while in the second, the statistics of the noise is described using recent development in texture modeling, the so called FRAME method. Given an a priori distribution of the mine parameters in combination with a trained noise distribution, a Bayesian detector is derived. Experiments indicate that significant gains in performance can be achieved as compared to the standard detector used, which correlates the infrared image with the known mine shape and thresholds the square of the output.
Shallowly buried object sand the background soil have different specific heat and heat conductivity. Thus, the thermal alternation over day and night produces a thermal contrast on the soil surface that can be detected by an IR sensor. We present a simple model for the spatio-temporal temperature signature of a buried land mine by means of a 3D Gaussian function. Such a mode is appropriate since both measured data and simulations based on the finite element method show a spatio-temporal behavior that strongly resembles a 3D Gaussian shape. An advantage of modeling the signature as a Gaussian shape is that objects with approximately the same size but with different physical properties can be obtained simply from a scaled version of the original model.
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